Prediction of English Online network performance based on Xgboost algorithm

Abstract

With the popularization of the Internet and the development of artificial intelligence technology, it is profoundly changing the direction of educational innovation, breaking the barriers between online and offline, and making the online education model develop rapidly. With the change of educational methods, how to evaluate the effectiveness of online education has become an urgent problem to be solved. This paper collects students' online learning behavior data through the online English learning platform, and uses visual analysis methods to further explore the influence mechanism of online learning behavior characteristics and academic performance, and analyzes the characteristics of different learning behaviors in different grades. The English online network score prediction based on the XGBoost algorithm in this paper can allow test takers to predict the test score range more conveniently and quickly, saving operation time and improving work efficiency. In this paper, the XGBoost algorithm is used to construct a grade prediction model for the selected learning behavior characteristic data, and then the model parameters are optimized by the grid search algorithm to improve the overall performance of the model, which in turn can improve the accuracy of students' English grade prediction to a certain extent. The final results of the study show that the prediction accuracy remains above 90%, indicating that the accuracy is high and has certain feasibility. The predicted score is slightly higher than the actual score, and the predicted score range should be appropriately reduced to ensure English scores.